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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  1/2008 - 6

Spectral Shaping for Codes with P.S.D. Expressed by Rational Functions

ALEXANDRU, N. D. See more information about ALEXANDRU, N. D. on SCOPUS See more information about ALEXANDRU, N. D. on IEEExplore See more information about ALEXANDRU, N. D. on Web of Science, ALEXANDRU, M. L. See more information about ALEXANDRU, M. L. on SCOPUS See more information about ALEXANDRU, M. L. on SCOPUS See more information about ALEXANDRU, M. L. on Web of Science
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Download PDF pdficon (358 KB) | Citation | Downloads: 611 | Views: 3,136

Author keywords
Codes, Digital filters, Linear predictive coding, Markov processes, Spectral analysis

References keywords
signals(4), digital(4), codes(4), alexandru(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2008-04-02
Volume 8, Issue 1, Year 2008, On page(s): 31 - 35
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.01006
Web of Science Accession Number: 000259903500006
SCOPUS ID: 67749131182

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Based on the fact that spectral shaping of digital data signals is obtained by encoding and that there exist spectrally equivalent digital filters, the method of Justesen was applied to derive digital filters that approximate the power spectral density (p.s.d.) of a code. The (2,2,3) code, which belongs to the class of FAS (Finite Autocorrelation Sequence) codes showing a limited number of values of the autocorrelation function that are not zero, was used as an example. The spectral properties of the (2,2,3) code are thoroughly investigated in terms of autocorrelation function values and p.s.d. both as a function of normalised frequency fn and the probability of a mark p. The prediction coefficients for the digital filter implementations are derived using approximations by Markov processes of the third, fourth and fifth order. The resulting spectra are compared with those of (2,2,3) code.

References | Cited By  «-- Click to see who has cited this paper

[1] G. L. Cariolaro and G Vannucchi, "Spectral density equivalent linear filter for a broad class of digital signals", Proc. of Internationl Conf. on Commun., San Francisco, pp.11-9-11.14, 1975.

[2] J. Justesen, "Information Rates and Power Spectra of Digital Codes", IEEE Transactions Inf. Theory, Vol.IT-28, No.3, May, pp. 457-472, 1982.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 52]

[3] V. A. Dieuliis and F. P. Preparata, "Spectrum Shaping with Alphabetic Codes with Finite Autocorelation Sequence", IEEE Trans. on Comm., vol.COM-26, No.4, pp. 474-477, April 1978.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 5]

[4] N. D. Alexandru. and G. Morgenstern. Digital Line Codes and Spectral Shaping, Editura Matrix ROM, Bucuresti, 1998

[5] N. D. Alexandru. and Dae Young Kim, "Spectral Shaping via Coding", CERMI, Iasi, 2003

[6] J. Justesen, "Calculation of power spectra for block coded signals", IEEE Trans. on Communications, Vol. 49, No 3, March, pp. 389-392, 2001.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]

[7] N. D. Alexandru and C. Chatellier, "Extending the Calculation of Correlation Function for 1I - nO Block Coded Signals", Proc. of the Int. Symposium SCS'2003, Iasi, pp. 629-632, 2003.

[8] J. G. Kemeny and J. L. Snell, "Finite Murkov Chains", Princeton, NJ: Van Nostrand, 1960.

[9] G. L. Cariolaro and G. P. Tronca, "Spectra of block coded digital signals", IEEE Trans. Commun. Technol., vol. COM-22, pp. 1555-1563, Oct. 1974.
[CrossRef] [SCOPUS Times Cited 91]

[10] N. D. Alexandru, V. Cehan and L. Pancescu, "An Extended Method for Calculating the Autocorrelation Function for 1I-2O Codes Based on Entry and Departure Matrices", International Conference Communications'2000, Bucharest, Romania, pp. 55-58, 2000.

References Weight

Web of Science® Citations for all references: 61 TCR
SCOPUS® Citations for all references: 151 TCR

Web of Science® Average Citations per reference: 6 ACR
SCOPUS® Average Citations per reference: 15 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

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